Teaching Statistics最新文献

{"title":"Writing goals in U.S. undergraduate data science course outlines: A textual analysis","authors":"Constance L. Gooding, Alex Lyford, G. Giaimo","doi":"10.1111/test.12314","DOIUrl":"https://doi.org/10.1111/test.12314","url":null,"abstract":"Instructors at postsecondary institutions have designed a myriad of data science classes to keep up with the rise of big data. Businesses and companies have become increasingly interested in hiring people with strong data acquisition, management, and communication skills. Since data science as a field of study is relatively new, though it has deep connections to statistical studies, there are few comprehensive analyses of data science classes, majors, programs, and curricular goals. Through this research, we analyze how writing and communication are taught in undergraduate data science classes in the United States. We analyze the presence of writing and communication learning goals from course descriptions and course syllabi. These results show that most data science courses emphasize technical, computing skills over writing, and communication skills. We conclude with a set of actionable heuristics that emphasize integrating writing and communication into data science courses so that students are prepared to use these skills as responsible citizens.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"44 1","pages":"110 - 118"},"PeriodicalIF":0.8,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49271717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UEFA EURO 2020: An exciting match between football and probability","authors":"Giulia Fedrizzi, Luisa Canal, Rocco Micciolo","doi":"10.1111/test.12315","DOIUrl":"https://doi.org/10.1111/test.12315","url":null,"abstract":"Football, as one of the most popular sports, can provide exciting examples to motivate students learning statistics. In this paper, we analyzed the number of goals scored in the UEFA EURO 2020 final phase as well as the waiting times between goals, considering censored times. Such a dataset allows us to consider some aspects of count data taught at an introductory level (such as the Poisson distribution), as well as more advanced topics (such as survival analysis taking into account the presence of censored times). Employing data from the final phase of UEFA EURO 2020, depending on the course level, the student will acquire knowledge and understanding of a range of key topics and analytical techniques in statistics, develop knowledge of the theoretical assumption underlying them and learn the skills needed to model count data.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"44 1","pages":"119 - 125"},"PeriodicalIF":0.8,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46264711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An applied statistics teaching lesson that uses NBA playoff data to illustrate uncertainty in sporting contests","authors":"Rotua Lumbantobing, Todd McFall","doi":"10.1111/test.12313","DOIUrl":"https://doi.org/10.1111/test.12313","url":null,"abstract":"In this article, we offer a teaching lesson on combinatorics and binary outcomes that utilizes real‐world data. The focus of the lesson is to teach students how to analyze the effects of the National Basketball Association's (NBA) 2003 decision to extend the first round of its postseason from a best‐of‐five series of games to a best‐of‐seven series using combinatorics and ideas about binary outcomes. Students conjecture how much longer series will make less certain the outcomes of these series and then use the 27 years of first‐round series results we provide to evaluate their conjectures on how series results have changed since 2003. After finishing this lesson, students will have a firmer grasp on applying combinatorics and binary outcomes to real‐world situations. This lesson is compatible with both traditional and remote classes and can be extended to other sports, making it a lesson for all academic seasons.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"44 1","pages":"104 - 109"},"PeriodicalIF":0.8,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46737253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistical edutainment: You're the subject for our next subject!","authors":"L. Lesser, Dennis K. Pearl","doi":"10.1111/test.12312","DOIUrl":"https://doi.org/10.1111/test.12312","url":null,"abstract":"Readers are invited to participate in a data collection exercise that will be used subsequently in this series.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"44 1","pages":"133"},"PeriodicalIF":0.8,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45623489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial Comment","authors":"Kassandra E. Zaila Ardines, Petar Bajic","doi":"10.1111/test.12047","DOIUrl":"https://doi.org/10.1111/test.12047","url":null,"abstract":"© 2014 The Authors Teaching Statistics © 2014 Te Teaching Statistics hats off to the G Oswald George prize winners 2013 for focussing attention on problems with excessive use of the term ‘population’ in statistics education and discussion of possible alternative approaches. In their article, Lu and Henning (2013) quote an oftenused classical definition of a population as ‘the set of all entities of interest in a particular statistical study’, discuss what is wrong with the population concept, include suggestions of terms such as ‘data generating process’ and the ‘statistical population’ of Kass (2011), and emphasize the importance of understanding and questioning the assumptions of a statistical model, and how a statistical model is a ‘lens’ (Wild, 2006). There is much discussion that can be opened here. Is the prolonged use of the word ‘population’ just habit, or is it because of avoiding words such as ‘theoretical’, ‘model’, ‘distribution’ or ‘parameter’, and lack of energy to find alternative expressions suitable for various student levels? Lu and Henning point to the problem of understanding randomness in experiments; the challenges of observational studies require even more discussion with students. In coming to understand concepts of random representativeness of data with respect to the issues of interest, the words ‘general situation or population’ can be very useful. For example, ‘if the data can be considered randomly representative of a general situation or population with respect to the issues of interest, then we can use the data to comment on the general situation or population with respect to these issues’. Clearly the word ‘population’ is appropriate for some statistical situations, particularly those involving sampling from real populations of entities whether they be marbles or people. Such situations tend to be how young students are introduced to both chance and data. Then the term ‘population proportion’, which does make sense, tends to appear and the challenges of sampling to estimate a population proportion provide rich scenarios for student thinking and learning, with focus on ‘worry’ questions rather than delusions created by emphasis on sampling schemes available to organisations with vast resources. Categorical data do indeed provide valuable doors to statistical concepts and thinking at a","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"36 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/test.12047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49207640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The development of statistical literacy among students: Analyzing messages in media articles with Gal's worry questions","authors":"D. Delport","doi":"10.1111/test.12308","DOIUrl":"https://doi.org/10.1111/test.12308","url":null,"abstract":"Real‐world data are fundamental to modern teaching methodologies that aim to improve statistical knowledge and reasoning in students. Statistical information is encountered in everyday life, such as media articles and involves real‐world contexts. However, information could be biased or (mis)represented and students should be concerned about the validity of such articles, as well as the nature and trustworthiness of the evidence presented, while considering alternative interpretations of the findings conveyed to them. Statistics educators could make use of media articles to create opportunities for students to reflect on such (mis)representations and build statistical literacy. The purpose of this article is to show how information and data on the Omicron COVID‐19 variant have been (mis)represented in the media and by government entities. I also demonstrate how these examples may be utilized in the statistics classroom as they relate to concepts covered in most basic statistics courses.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"45 1","pages":"61 - 68"},"PeriodicalIF":0.8,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43667283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A primer on simple measures of association taught at undergraduate level","authors":"J. Allison, L. Santana, I. Visagie","doi":"10.1111/test.12307","DOIUrl":"https://doi.org/10.1111/test.12307","url":null,"abstract":"This article discusses and contrasts the measures of association introduced by Pearson, Spearman, and Kendall, as these are the three most commonly used in practice and also the ones primarily covered in introductory statistics courses. Emphasis is placed on concepts pertaining to the measurement of the level of association between two variables, the calculation of the coefficients, and the interpretation of the calculated values. In particular, we demonstrate how Spearman's rho and Kendall's tau can be expressed in terms of Pearson's correlation coefficient based on transformed data. Important concepts and potential pitfalls are illustrated using numerical examples.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"44 1","pages":"103 - 96"},"PeriodicalIF":0.8,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43994952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Teaching chance for real","authors":"H. MacGillivray","doi":"10.1111/test.12306","DOIUrl":"https://doi.org/10.1111/test.12306","url":null,"abstract":"This should read “teaching probability for real” but that would not attract as much attention, and attention is needed more than ever. Although most recent and most appalling news has been focussed elsewhere, readers may have seen reports on the severe flooding in Australia, in Queensland and New South Wales (NSW) regions. To give you some idea, Brisbane received 80% of its average annual rainfall in 3 days, and the flood mitigation dam built after the 1974 floods held back “four Sydney harbours worth of water”. The town of Lismore in northern NSW is no stranger to floods, but this flood was 2-3 m above all previous records, peaking at a new record height of 14.4 m. Flooding had also been experienced in some of the same regions a year ago. For the second year in a row, therefore, much has been written and debated in the media about floods, with politicians claiming it was a 1-in500 year or 1-in-1000 year flood (see, for example, Bureau of Meteorology shoots down NSW Premier Dominic Perrottet’s ‘one-in-1000-year’ flood claim j Sunrise (7news.com.au)), and with both risk assessment experts and media commentators saying that giving risk as “1-in-100 years” is “disastrous and meaningless”, accompanied by repeated explanations of what it means and what it does not. Some years ago, a medical specialist colleague whose work requires almost daily explanations to individual women of risks, both of future health and associated with treatments, told me that she now always gives risks in terms of probabilities, usually as percentage chance, having realised some considerable time ago that explaining in terms of 1-in-100 or 1-in-1000 was misleading and unfair to patients. Before readers reach for various pieces of literature to quote expertise to contradict the above, I am not advocating removing such expressions from teaching, as they play a valuable part in an overall, better balanced and less limiting approach. There are a number of useful lessons and messages in unpacking the above, but two key messages and advocacies are that:","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47507348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Issue Information","authors":"","doi":"10.1111/test.12269","DOIUrl":"https://doi.org/10.1111/test.12269","url":null,"abstract":"","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2022-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43007139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictors allowing early discharge after interventional treatment of acute coronary syndrome patients.","authors":"Dávid Bauer, Marek Neuberg, Markéta Nováčková, Petr Mašek, Viktor Kočka, Zuzana Moťovská, Petr Toušek","doi":"10.1093/eurheartjsupp/suac002","DOIUrl":"10.1093/eurheartjsupp/suac002","url":null,"abstract":"<p><p>Many scoring systems for predicting the outcomes of patients with acute coronary syndrome (ACS) have been proposed. In some populations, a significant reduction in length of hospital stay may be achieved without compromising patient prognoses. However, the use of such scoring systems in clinical practice is limited. The aim of this study was to propose a universal list of predictors that can identify low-risk ACS patients who may be eligible for an earlier hospital discharge without increased short-term risk for major adverse cardiac events. A cohort of 1420 patients diagnosed with ACS were enrolled into a single-centre registry between October 2018 and December 2020. Clinical, laboratory, echocardiographic, and angiographic measurements were taken for each patient and entered into the study database. Using retrospective univariant analyses of patients treated with percutaneous coronary intervention (PCI) (<i>n</i> = 932), we compared each predictor to 30-day mortality rate using the Czech national registry of dead people. Eleven predictors correlate significantly with 30-day survival: age <80 years, ejection fraction >50%, no cardiopulmonary resuscitation, no mechanical ventilation needed, Killip class I at admission, haemoglobin levels >110 g/L while hospitalized, successful PCI procedure(s), no residual stenosis over 90%, Thrombolysis in Myocardial Infarction 3 flow after PCI, no left main stem disease, and no triple-vessel coronary artery disease. In all, presence of all predictors applies to 328 patients (35.2% of the cohort), who maintained a 100% survival rate at 30 days. A combination of clinical, echocardiographic, and angiographic findings provides valuable information for predicting the outcomes of patients with all types of ACS. We created a simple, useful tool for selecting low-risk patients eligible for early discharge.</p>","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":"38 1","pages":"B10-B15"},"PeriodicalIF":1.7,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8971740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85512120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}